Figueroa, Jonine D

Abstract Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103–14. ©2016 AACR.

AbstractAutomated methods are needed to facilitate high‐throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large‐scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p‐value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p‐value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre‐ and post‐analytical quality control procedures are necessary in order to ensure satisfactory performance.

Abstract Introduction: Increased levels of circulating estrogens are associated with elevated risks of postmenopausal breast cancer. Previously, we showed that levels of sex steroid hormones in breast adipose tissue are positively correlated with circulating hormones. Further evaluation of hormone levels in breast tissues may help to explain the relationship between estrogens and breast cancer risk. Specifically, data suggest that focal breast lesions consisting of macrophages encircling dying adipocytes, referred to as “crown-like structures (CLS)”, are associated with increased aromatase activity, which could increase conversion of androgens to estrogens locally in the breast. Therefore, we assessed whether the presence of CLS affects the correlations between blood and breast adipose tissue hormone levels. Methods: Participants included 84 postmenopausal women with invasive breast cancer in the Polish Breast Cancer Study, a population based case-control study conducted from 2000 to 2003. We previously reported sex steroid hormone levels in cancer-free breast adipose tissue removed as part of breast excisions performed for cancer. Here, using immunohistochemical stains, we evaluated the number of CD68 positive macrophages and the presence of full CLS (i.e., complete adipocyte encirclement) in benign breast tissue sections prepared from surgical pathology specimens containing breast cancer. The relationship between the number of CD68 positive macrophages per unit area of fat and hormone levels was examined using Spearman rank correlations (rs). We assessed potential effect modification of the association between blood and tissue hormone levels by presence or absence of CLS by including an interaction term in linear regression models. Results: The number of CD68 positive macrophages per unit area of fat was not significantly correlated with levels of estrone, estradiol, androstenedione or testosterone measured in blood (rs = 0.001, -0.04, -0.07, and -0.09, respectively; p>0.05) or breast adipose tissue (rs = -0.15, 0.06, -0.06, and -0.08, respectively; p>0.05). Correlations between serum and adipose tissue hormones did not statistically significantly differ between women with CLS (n = 30) compared to those without CLS (n = 54). CLS were found more frequently in breast adipose tissue from overweight (i.e., women with a body mass index (BMI) between 25 and 29.9) (43%) and obese women (BMI >30) (30%) versus normal weight women, with (BMI between 18.5 and 24.9), (27%); (p = 0.04, χ2 test). The number of CD68 positive macrophages per unit area of fat was not significantly related to BMI (p = 0.31). Conclusion: Among postmenopausal women with invasive breast cancer, the presence of CLS was more common among overweight and obese women, but CLS did not influence the correlation between circulating and breast adipose tissue sex steroid hormone levels. Citation Format: Maeve Mullooly, Hannah P. Yang, Roni T. Falk, Sarah Nyante, Renata Cora, Ruth M. Pfeiffer, Derek C. Radisky, Daniel W. Visscher, Lynn C. Hartmann, Amy C. Degnim, Frank Z. Stanczyk, Jonine F. Figueroa, Montserrat Garcia-Closas, Jolanta Lissowska, Melissa T. Troester, Louise A. Brinton, Mark E. Sherman, Gretchen L. Gierach. Investigation of the relationship between crown-like structures and adipose tissue hormone levels among postmenopausal women with breast cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2767. doi:10.1158/1538-7445.AM2015-2767

AbstractBreast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large‐scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65–70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose‐response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96–98%), but yielded many false positives (positive predictive value = 30–32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large‐scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker‐specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

Abstract Background: Terminal duct lobular units (TDLU) are the predominant source of breast cancers. Lesser degrees of age-related TDLU involution have been associated with increased breast cancer risk, but factors that influence involution are largely unknown. We assessed whether circulating hormones, implicated in breast cancer risk, are associated with levels of TDLU involution using data from the Susan G. Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (2009–2011). Methods: We evaluated three highly reproducible measures of TDLU involution, using normal breast tissue samples from the KTB (n = 390): TDLU counts, median TDLU span, and median acini counts per TDLU. RRs (for continuous measures), ORs (for categorical measures), 95% confidence intervals (95% CI), and Ptrends were calculated to assess the association between tertiles of estradiol, testosterone, sex hormone–binding globulin (SHBG), progesterone, and prolactin with TDLU measures. All models were stratified by menopausal status and adjusted for confounders. Results: Among premenopausal women, higher prolactin levels were associated with higher TDLU counts (RRT3vsT1:1.18; 95% CI: 1.07–1.31; Ptrend = 0.0005), but higher progesterone was associated with lower TDLU counts (RRT3vsT1: 0.80; 95% CI: 0.72–0.89; Ptrend < 0.0001). Among postmenopausal women, higher levels of estradiol (RRT3vsT1:1.61; 95% CI: 1.32–1.97; Ptrend < 0.0001) and testosterone (RRT3vsT1: 1.32; 95% CI: 1.09–1.59; Ptrend = 0.0043) were associated with higher TDLU counts. Conclusions: These data suggest that select hormones may influence breast cancer risk potentially through delaying TDLU involution. Impact: Increased understanding of the relationship between circulating markers and TDLU involution may offer new insights into breast carcinogenesis. Cancer Epidemiol Biomarkers Prev; 23(12); 2765–73. ©2014 AACR.

ABSTRACTGenes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene‐environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P‐values = 3 × 10−07), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m2 (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m2 or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10−05). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci.